The Case of Thailand - JICA

6 1

Chapter 6

Social Infrastructure Demand for Low Income Housing

1 Introduction

In this chapter, we would like to project the demand for low income housing need

and see whether they can be affordable with income growth in Thailand Firstly, a simple

household profile is narrated We also show a simple regression analysis which applies

surveyed data from the Household s Socio Economic Survey SES to test a hypothesis of

ownership' Later, a comprehensive model is proposed with policy scenarios

1 1 Household Profile

Base on Household's Socio Economic Survey 2015, the profile of approximately

43,000 households' sample is summarized as follows

According to the SES 2015, the average household size is relatively small to 2 8 persons per

household skewness 0 817 The mean age of household head is relatively normal with

mean 53 87 year old skewness 0 002 It should be noted that household size in Thailand

has become smaller than in the past not shown here

Figure 6 1 Distribution of Household Members 2015

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

1 2 3 4 5 6 7 8 9

Historgram of Households' Members

Nu

mb

er

of

sa

mp

les

Household's member

Source SES 2015

6 2

Figure 6 2 Normal Density Distribution of Age of Household s head

.000

.004

.008

.012

.016

.020

.024

.028

0 10 20 30 40 50 60 70 80 90 100 110

De

nsit

y

Age of HH head in year

Source SES 2015

We have investigated the age distribution of household s head and found that it is

normally distributed as shown

Figure 6 3 Histogram of Household Size by Age of Head

0

40

80

120

160

200

10 20 30 40 50 60 70 80

Histogram of member=1 person by age of head

0

100

200

300

400

500

10 20 30 40 50 60 70 80


0

100

200

300

400

10 20 30 40 50 60 70 80


0

50

100

150

200

250

10 20 30 40 50 60 70 80


0

20

40

60

80

100

120

10 20 30 40 50 60 70 80


0

10

20

30

40

50

60

70

10 20 30 40 50 60 70 80


0

10

20

30

40

10 20 30 40 50 60 70 80


0

4

8

12

16

20

10 20 30 40 50 60 70 80


Number of Household Member by

Age of Head

Source: Household Socioeconomic Survey, 2015

6 3

Figure 6 4 Household Ownership by Age of Head

0

40

80

120

160

200

240

280

320

10 20 30 40 50 60 70 80 90 100

Not Owning House Histogram

0

200

400

600

800

1,000

1,200

10 20 30 40 50 60 70 80 90 100

Owning House Histogram

House Ownership by Age of Head

Given the age of household head's distribution, we plot the histogram of household

size by a number of members i e , a size where the age of head is around the mean age It is

found that household member distribution in relatively normal bell shape , except the size

of 1 member household

Figure 6 5 House Ownership Tenure Characteristics

0

200

400

600

800

1,000

1,200

10 20 30 40 50 60 70 80 90 100

Own dwell ing and land

0

10

20

30

40

50

10 20 30 40 50 60 70 80 90 100

Own dwelling on rented land

0

4

8

12

16

20

24

28

32

10 20 30 40 50 60 70 80 90 100

Own dwelling on public area

0

5

10

15

20

25

30

10 20 30 40 50 60 70 80 90 100

Hire - purchased

0

50

100

150

200

250

10 20 30 40 50 60 70 80 90 100

Rent

0

10

20

30

40

50

60

70

10 20 30 40 50 60 70 80 90 100

Rent paid by others

0

10

20

30

40

10 20 30 40 50 60 70 80 90 100

Occupied / rented free

House Tenure Types by Age of Head

Fre

qu

en

cy o

f D

istr

ibu

tio

n

Source: Household Socioeconomic Survey 2015, NSO

6 4

The ownership of a house is distributed normally with age of head A household with

a younger mean age of head has a lower probability to own house House tenure by age of

head indicates that ownership of the house by type of dwelling on own land, rented land, as

well as public land, are normally distributed across age of head Households with tenure as

rent' and hire purchase' have a younger age of head

Figure 6 6 Positive Relationship of Ownership Tenure Scattered plot by controlled by age

of head

0

200

400

600

800

1,000

1,200

1,400

0 200 400 600 800 1,000 1,200

Own dwelling and land

No o

f all

earn

ers b

y age

Relationship of Earners Numbers and House Tenure

(by Age of Head)

The household formation mentioned above can be further analyzed in terms of the

economic behavior The most crucial determinants of housing need are income and or

expenditure of households Households income distribution in 2015 is approximately

followed the log normal distribution This implies that most of the households belong to

lower income ranges The mean income is 23,464 baht per month while median income is

17,316 baht per month respectively

6 5

Figure 6 7 Household Income followed the Log Normal Distribution

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

9,000

0 20000 40000 60000 80000 100000 120000 140000

Distribution of Hoseholds Income 2015

Note: Mean income 23,464 baht per month

Median income 17,316 baht per month

Jarque-Bera 115828; skewness 2.24; Kertosis 9.68

No. of samples 42779

Source: SES 2015

Fre

qu

en

cy o

f n

o.

of

ho

use

ho

lds

Figure 6 8 Household Income Distribution Histogram plot controlled by age of head

0

100

200

300

10 20 30 40 50 60 70 80 90 100

Income range <10,000 baht per month

0

10

20

30

40

50

10 20 30 40 50 60 70 80 90 100

HH income >90,000 baht by age of head

0

100

200

300

400

500

10 20 30 40 50 60 70 80 90 100

Income range 10,000-20,000 baht per month by age of hh head

0

40

80

120

160

200

240

280

10 20 30 40 50 60 70 80 90 100

HH Income 20,000 - 30,000 baht per month by age HH head

0

40

80

120

160

10 20 30 40 50 60 70 80 90 100

HH Income 30,000-40,000 by agd of Head

0

20

40

60

80

100

10 20 30 40 50 60 70 80 90 100

HH income 40,000-50,000 baht per month by age of head

0

10

20

30

40

50

60

10 20 30 40 50 60 70 80 90 100

HH income 50,000-60,000 baht per month by age of head

0

10

20

30

40

10 20 30 40 50 60 70 80 90 100

HH income 60,000-70,000 baht by age of head

0

10

20

30

40

10 20 30 40 50 60 70 80 90 100


0

5

10

15

20

10 20 30 40 50 60 70 80 90 100


Income Distribution by Age of Head 2015

Fe

qu

en

cy o

f D

istr

ibu

tio

n

Age of Head of Household (year old)

6 6

Figure 6 9 Household Expenditure followed the Log Normal Distribution

0

1,000

2,000

3,000

4,000

5,000

6,000

0 12500 25000 37500 50000 62500 75000 87500 100000

HH Average expenditure in baht per month

Fre

qu

en

cy o

f d

istr

ibu

tio

n

Source SES 2015

Figure 6 10 Relationship between Household Income and Expenditure 2015

150,000

100,000

50,000

35,000

20,000

10,000

5,000

3,500

2,000

1,000

500

200,00050,00020,0005,0002,000500

incomehh_abs

Exp

enditure

HH

_abs

Household Income and Expenditure 2015

(Bath per month)

We have estimated the determinant of ownership of a house The owning ratio or

ownership ratio is ratio between number of households with status of house ownership

over the summation of households owning house and those with rental status is determined

6 7

from right hand variables These are households belong to income deciles 1 4 and those who

belong to income deciles 5 10th ΣINCi See Table 6 1

We have applied the Generalized Method of Moment GMM after controlled by age

of head to get rid of over identification problem' We have controlled for the endogenous

biased by applying instruments variables on house types TYPEj as well as member sizes Σ

MEMBERh

The estimation result indicates that household with income 1 4 classes have a

significant negative relationship with ownership ratio The household with income 5 10

classes show a significant positive relationship with the ownership ratio

An uncontrolled version of regression applying logistic model which included the

dimension of location has found similar results For the municipal area, the model indicates

that probability of being house ownership has a positive relationship with total income and

age but negative relationship with members of the family of an individual These mean as

total income, age or a percentage change in age increase, a person will have higher

tendency to own a house In addition, as the number of members in family increases, a

person will have lower tendency to own a house for municipal area

For Bangkok area and vicinities, the probability of owning a house has a positive

relationship with total income and age but negative relationship with members of the family

of an individual In addition, as the number of members in family increases, a person will

have lower tendency to own a house for Bangkok area and vicinities

In conclusion, as people become older, they want to purchase their own houses for

observations from the entire country, municipal area, and Bangkok area and vicinities

However, the higher total income of an individual induces purchasing a house only in the

municipal area, and Bangkok area and vicinities Surprisingly, a number of members in a

family is significantly associated with lower tendency to purchase houses for all 3 groups of

observations

6 8

Table 6 1 Determination of House Ownership Ratio of Owning House Rental House

Status

Dependent Variable OWNING_RATIO

Method Generalized Method of Moments

i income class by deciles i 1,2, 10 open ended; j house type j 1 7; and h household member h 1, 8 and

over

Instrument specification TYPE1 TYPE2 TYPE3 TYPE4 TYPE5 TYPE6

TYPE7 MEMBER1 MEMBER2 MEMBER3 MEMBER4 MEMBER5

MEMBER6 MEMBER7 MEMBER8_OVER

A 'Constant' term is added to instrument list

Lagged dependent variable & regressors added to instrument list

Variable Coefficient Std Error t Statistic Prob

C 1 004455 0 040335 24 90311 0 0000

INC1 INC2 INC3 INC4 9 30E 05 3 30E 05 2 816813 0 0064

INC5 INC6 INC7 INC8 INC9 INC10_OPEN 0 000489 0 000174 2 809408 0 0065

AR 1 0 955577 0 008733 109 4258 0 0000

R squared 0 990279 Mean dependent var 0 745002

Adjusted R squared 0 989844 S D dependent var 0 266097

S E of regression 0 026817 Sum squared resid 0 048182

Durbin Watson stat 2 518718 J statistic 11 93173

Instrument rank 18 Prob J statistic 0 611786

Inverted AR Roots 96

Source this study, applying SES 2015

Table 6 2 Logistic Regression Output of House Ownership for Entire Country

Note 1 legend p< 1; p< 05; p< 01

1. Full model full , which includes all independent variable 2 Log full model L_full ,

Variable Full l_full drop_members drop_age

ttlinc 5 50E 07 4 19E 07 6 208e 06

members 27902681 30645988

age 08957908 09061646

l_ttlinc 22138641

l_members 68900411

l_age 3 4675945

_cons 3 8485078 11 106518 4 760824 42335504

N 5376 5375 5376 5376

aic 15873848 15937594 16232632 18433407

bic 15873874 15937621 16232652 18433426

6 9

2. which includes natural log of all independent variable 3 Drop members , which excludes a

number of the family member from the model

Table 6 3 Logistic Regression Output of House Ownership for Municipal Area

Variable

full l_full drop_membe

rs

drop_age

ttlinc 7 970e 06 8 122e 06 0000148

members 09741994

11781796

age 09581992 09577326

l_ttlinc 0 0915607

l_members 0 13988323

l_age 3 8909211

_cons

5 3455629 16 623346 5 6475818 1 6136944

N 2538 2537 2538 2538

aic 5046903 9 5068449 9 5063470 7 5919845 9

bic 5046927 3 5068473 3 5063488 2 5919863 4

Table 6 4 Logistic Regression Output of House Ownership for

Bangkok Area and Vicinities

Variable full l_full drop_members drop_age

ttlinc 00001377 00001382 00001935

members 14121318 11503283

age 10279559 10134006

l_ttlinc 61156397

l_members 0 32601779

l_age 4 2479191

_cons 5 9516206 23 080995 6 3428299 2 0298528

N 1635 1635 1635 1635

aic 2597053 9 2542555 6 2613837 1 3077564 5

bic 2597075 5 2542577 2 2613853 3 3077580 7

legend p< 1; p< 05; p< 01

6 10

2 Low Income Housing Needs and Affordability Model

The model starts with the population projection 2015 2030 Here also, given the

population by single year age 'a' assuming fertility rates , gender 's' male, female , we

project the household h intact, one person, single head, and other household types

respectively The brief description of the projection modules used in this study is as

follows

1) Population Module

The number of households by type h and age a is determined from the population

by single age population multiplied by headship rate

HHa,h hsa,h,s PoPa,s

Number of household by type 'h' is a summation of household by single age a

HHh ΣaHHa,h

Total number of household

HH ΣHHh

HH total number of households,

HHh number of household formation by type h,

HHa,h number of household formation by age a, type h, and

hsa,h,s headship rate to form household type h i e , the rate of family formation

PoPa,s Population with single age a, and gender s, over the forecasting horizon

t 2015 2030

2) Housing need from the household formation demand side

Housing inventories at a point in time HI are determined by the number of

households, assuming one household would need one house unit Since there are vacancies

of house units during the forecasting horizon, the gross house inventory stock is the

summary of basic need' of house stock equivalent to a number of households adjust by

vacant house unit 0<av<1 at a point in time The result is net house inventory stock

6 11

In reality, households may reside together in one house unit We, therefore, adjust

the number housing need with doubling up rate' 0<af<1 to get the adjusted number of net

house inventory

HI af 1 av HH

HI adjusted house inventor stock net

af doubling up rate, 0<af<1

av vacancy rate, 0<av<1

We are interested in the housing need at each time period t year The change in

housing inventory or incremental housing need in each sub period year is therefore

△HIt HIt HIt 1

Housing withdrawal owing to replacement age of house stock is determined by

withdrawal rate aw at each time t, from existing house inventory HIt

WHt aw HIt

The housing 'start' would be constructed to replace the withdrawal units and to fulfill the

inventory change This new housing needs or housing start HSSt is determined as

HSSt WHt △HIt

3) Affordability of housing need

The household's affordability of housing need is not automatic Normally, the low

income household is not able to access the private housing market Low income household

such as those belonging to income deciles 1 5th class may face with income and saving

constraint A low income household cannot do monthly mortgage service with the short

term loan, high market interest rate, high down payment, and high market's house price The

following affordability module will be used in our study to arrive at feasible public policy

on social infrastructure investment of Thailand in the next decades

GDPR Gross Domestic Product at Constant Price

PGDP GDP deflator or general price e level

Ym Average monthly mean income from SES

6 12

Ymh monthly mean income of household h th h intact, single head, one person, and

others type of households

Ymh,i monthly mean income of household h th, income class i th i 1,2, 10

YDh,i Disposable income of household h th, income class i th i 1,2, 10

@ Adjustment coefficients between monthly income survey by the NSO and estimated by

the National Accounts NESDB

dh Coefficient of total average income and average income of each household h th

dhi Coefficient of income distribution of household h th by income class i th, i 1,2,3,

10

Phi Probability that any household belongs to income class i th in household type h th

N Z; 0,1 standard normal distribution with mean and variance 0,1

Zhi Standard score of random variable of income of the function N Z; 0,1 of household h

th

Uh Mean income of household h th which has income distribution function as a log normal

Distribution function

SD standard deviation of income of household h th

Step 1 Household Income Projection by income class

This module identifies the income of household h th by income class i th Note that

1 time subscript is omitted for sake of simplicity 2 The growth of income per head

projected by Macro econometric model or published by official sources NESDB, BOT

over the planning horizon 2015 2030 can be used for projection of the left hand side

variables

Ym @GDPR PGDP

Ymh dh Ym

Ymh,i dh,i Ymh

HEh,i eh,i Ym h,i

HSEh,i sheh,i HEh,i

6 13

HEh,i Income of class i th i 1,2,3, 10 of Household h th

which can be disposed for household expenditure

HSEh,i Income of class i th i 1,2,3, 10 of household h th, which can be disposed for

housing expenditure

eh,i Ratio of income in each class i th i 1,2,3, 10

which can be disposed in general by household h th,

sheh,i Ratio of expenditure of household h th in income class i th i 1,2,3, 10

disposed for housing expenditure

Step 2 Projection of household expenditure on housing acquisition

NHEh,i 1 reh,i HSEh,i

NHEh,i Household expenditure on housing by household h th, income class i th

i 1,2,3, 10 This expenditure is inclusive of household s saving for down

payment in hire purchase of house

reh,i recurring expenditure by household h th, income class i th i 1,2,3, 10

Step 3 Projection of housing affordability through monthly mortgage service Service can

be allocated from household saving after recurring expenditure

MGS h,i NHEh,i HEh,i Ymh,i

MGSh,i monthly mortgage service of household h th in each income class i th

i 1,2,3, 10

The capitalization factor CF is found to be

CF = {1 – (1+r)−T}/𝑟

We can evaluate the capital value of house of household h th in each class i th i 1,2,3, 10

AFh,i = {(𝐶𝐹)(𝑀𝐺𝑆ℎ,𝑖)}/{1−𝑑𝑝/100}

Given the post finance parameters as follows

r annual rate of interest in mortgage service which government subsidy can be intervened,

T term loan in year

6 14

dp Percentage of down payment before mortgage service

4) Government Low income housing Policy

NHh,i ph,i NHh

NHh,i Number of household type h th which belong to deciles class of i 1,2,3,4 where i th

is lower than affordability level with probability ph,i

Given the availability of data from government and private sources as

(1) Household data surveyed by the National Statistical Office, namely

Household Socio Economic Survey several years to estimate the necessary parameters

mentioned above

(2) The official population projection 2015 2040 is from National Economic and

Social Development Board

(3) We apply our macroeconomic model to forecast the GDP growth and derive

the mean income of households at a national level

(4) The mean income is transformed to the monthly income of household to

match with a month income baseline from SES

(5) House price data is randomly selected from private housing market sources

(6) Other financial data are from government sources like Government Housing

Bank, Government Saving Bank and the Bank of Thailand etc

The government NHA can propose the Ministry of Human Security and Social

Welfare the number of housing needs of the low income group The simulation of policy

instruments can be tried to arrive at possible solution and cost of social infrastructure

investment as well as the cost of policy intervention

3 Low Income Housing Needs and Affordability, Model Simulation

Population Projection

6 15

The changing structure of household and income distribution in Thailand determines

the demand for housing Firstly, we applied an official number of the population projected

by NESDB1it's under the assumption of declining fertility

The population projection series from the NESDB is shown in the graph below It is

clearly shown that the urban household is growing to substitute for the rural household in

the coming decades Thus, urban housing policy is a very crucial issue Secondly, we have

drawn a number of households by types i e , Intact', Single head', One person', and Others'

from the Population Census 2010 and related reports of NESDB 2015 2050 Given headship

rates the parameters to signify the probability to be household head over the total number of

households, we obtain the household by types of household s head The number of

households by types is shown in tables 6 5 below

Figure 6 11 Population Trend in Thailand, Urban and Rural Area 2010 2050

20,000

30,000

40,000

50,000

60,000

70,000

80,000

2010 2015 2020 2025 2030 2035 2040 2045 2050

Population in 1,000 persons 2010-2050

Population in rural area in 1,000 persons

Urban population in 1,000 persons

Population Projection: Total, Urban and Rural (in 1,000 persons)

(1,0

00

pe

rso

ns)

1 The National Economics and Social Development Board, Population Projection Thailand

6 16

Figure 6 12 Projection of Monthly Households Income 2016 2050

10,000

20,000

30,000

40,000

50,000

60,000

70,000

80,000

90,000

2010 2015 2020 2025 2030 2035 2040 2045 2050

Projection of Monthly Households' Income 2016-2050

(Base Year's Median monthly income in 2015 is 17,316 baht per month)B

ath

pe

r M

on

th

From the macro econometric model, we have forecasted the real and nominal GDP

and other macro variables From the reference path of income at the national level,

household numbers, we have estimated the mean income per month earned by an average

household From this information, we use the probability model to estimate the distribution

of households by income percentiles A number of households in all classes accepts class3th

are projected to increase over time 2015 2050 From a policy point of view, poorest class 1st

and 2nd are not able to mobilize to higher classes and need to be continuously taken care by

the public residential system The rest of households may be able to enter the housing

market via rent, hire purchase if with public debates and subsidies For class 6th 10th, we

expect that their demand for housing will be borne by own savings and private house market

with market base financial cost

6 17

Figure 6 13 Household Distribution by Income Class in 2015 2050

200,000

400,000

600,000

800,000

1,000,000

1,200,000

1,400,000

1,600,000

2010 2015 2020 2025 2030 2035 2040 2045 2050

Class 1 (Percentile 10)

0

1,000,000

2,000,000

3,000,000

4,000,000

2010 2015 2020 2025 2030 2035 2040 2045 2050


0

1,000,000

2,000,000

3,000,000

4,000,000

5,000,000

6,000,000

2010 2015 2020 2025 2030 2035 2040 2045 2050


0

400,000

800,000

1,200,000

1,600,000

2010 2015 2020 2025 2030 2035 2040 2045 2050


400,000

800,000

1,200,000

1,600,000

2,000,000

2010 2015 2020 2025 2030 2035 2040 2045 2050


200,000

400,000

600,000

800,000

1,000,000

1,200,000

2010 2015 2020 2025 2030 2035 2040 2045 2050


0

400,000

800,000

1,200,000

1,600,000

2,000,000

2010 2015 2020 2025 2030 2035 2040 2045 2050


400,000

800,000

1,200,000

1,600,000

2,000,000

2010 2015 2020 2025 2030 2035 2040 2045 2050


200,000

300,000

400,000

500,000

600,000

700,000

800,000

2010 2015 2020 2025 2030 2035 2040 2045 2050


0

200,000

400,000

600,000

800,000

2010 2015 2020 2025 2030 2035 2040 2045 2050


Number of Urban Households by Income Class 2015-2050

(in unit of Household)

Source: Model Projection by this study

Assumptions of Model Parameters

1 Doubled up rate trend is different among household types and between HH urban

and total HH 2 Distribution of household s types change over time i e , lower share of

intact household' and replaced by the rising share of Single head' as well as One person

and Others' households respectively 3 Withdrawal rate and vacancy rate are assumed as its

past trend 4 Total household's mean income is consistent with national GDP's growth

trend This is obtained from macroeconomic model forecasting 5 The mean income by

percentile income class is obtained from applying SES household income distribution in

2015

We start from the household formation into 4 types as mentioned above The

household would need at least one dwelling It can be doubled up with other households i e ,

more than one household in one dwelling Households can own various types of houses

either single house, twin house, flat, condominium etc They can have many ownerships

status from owning a house, having rental status, live on own land plot but build own house

or rent land and house or build a house on government land etc The most serious urban

problem is however urban poor has to encroach on public land e g , state enterprises' land

property like national railway land around the railway station or along the rails on both

sides They once were a construction labor in public project and decided to settle down on

public land along the canal How to provide both dwelling and jobs to these urbanites are

serious social issues in every developing country Thus, housing needs in case of developing

countries are not totally congruent with the definition in developed countries Therefore, in

6 18

model forecasting it is difficult to find proper parameters like housing withdrawal' vacancy

rates' as well doubled up rate' etc

In our study, we have applied the simple model of housing needs and affordability

believed to be consistent with the situation in Thailand and many other Asian countries

Firstly, the projection of housing inventory would be sufficed to calculate using a

spreadsheet as below The changing of housing inventory adjusted by housing withdrawal

and vacancy rate can be projected With further calculate the change in inventory; we finally

obtain the housing start to be built to fulfill house need from the population and human

settlement concept We are interested in the settlement in the urban area The housing starts

to be planned for human settlement in the urban area as incremental from the past inventory

stock is found in the row of Table 6 5, it is in the magnitude of 400 500 thousand units

approximately for each sub period Bear in mind that these housing starts are for all income

classes either rich or poor households We are interested only in the low income housing

provision namely those who cannot enter the private housing market They may have to

either rent government house or heavily subsidized for hire purchase with lengthy of

mortgage services say 30 years with affordable lower than market interest rates, and down

payment

In order to match household needs with affordability, we need a projection of future

mean income From official projection at the national level, it can be translated into the

level of mean income of household by income class Next step is to follow the matching of

mortgage services per month with housing expenditure for house payment as shown in the

system of equations above

6 19

Table 6 5 Total Households by Types and Housing Inventory and Housing Start 2009 2037

2009 2015 2020 2025 2030 2035 2037

Total household 1,000 units

19,579

21,326

22,535

23,599 23,603 23,882

23,991

Intact household

13,848

13,917

13,851

14,387 12,501 10,380

9,708

one person household

1,442

2,492

3,268

3,540 4,721 5,851

6,238

single head household

4,281

4,909

5,408

5,664 6,373 7,642

8,037

others household

9

8

8

8 8 9

9

Intact household share 100.00 100.00 100.00 100.00 100.00 100.00 100.00

one person household share 70.73 65.26 61.46 60.96 52.96 43.46 40.46

single head household share 7.36 11.69 14.50 15.00 20.00 24.50 26.00

others household share 0.05 0.04 0.04 0.03 0.03 0.04 0.04

Urban household 1,000 units

6,485

7,572

8,648

9,839 10,671 11,649

12,045

Rural household 1,000 units

13,094

13,754

13,887

13,761 12,932 12,233

11,945

1 Intact household 1,000 units

intact household urban

4,587

4,941

5,382

6,063 6,151 5,823

5,717

AF doubling rate

0 42

0 42

0 45 0 50 0 65 0 75

0 75

AF Urban

1 00

1 00

1 00 1 00 1 00 1 00

1 00

AV vacancy rate

0 02

0 02

0 02 0 02 0 02 0 02

0 02

adjusted house inventory stock

HI intact

AF 1 AV sumHH at t

5,670

5,698

6,093

6,741 7,858 7,586

7,089

change in adjusted house

inventory stock delta_HI intact

146 26

179 465 158 161


HI urban intact

AF 1 AV sumHH at t

4,518

4,867

5,301

5,972 6,059 5,736

5,631

6 20


inventory stock delta_HI urban

intact

102

107

300 34 24

13

withdrawal rate aw 2 00 2 00 2 00 2 00 2 00 2 00

No of housing withdrawal

WHt intact aw HI

114

122

135 157 152

142


WHt urban intact aw HI

4

6

7 11 14

16

housing start HS intact WHt

delta_HI

260

95

314 622 6 19

housing start HS urban

intact WHt delta_HI

216

229

435 191 175

155

2 One person household 1,000

units



WHt one person aw HI

12,396

16,256

17,611 23,485 29,109

31,032


WHt urban one person aw HI

4,401

6,238

7,342 10,618 14,198

15,581

housing start HS one person

WHt delta_HI

552

166 182 259 280

164

housing start HS urban one

person WHt delta_HI

193

81 51 148 176

126

3 single head household 1,000

units

single head household urban

1,411

1,734

2,065

2,349 2,867 3,709

4,015

AF doubling rate

1 00

1 00

1 00 1 00 1 00 1 00

1 00

AF Urban

1 00

1 00

1 00 1 00 1 00 1 00

1 00

AV vacancy rate

0 01

0 01

0 01 0 01 0 01 0 01

0 01


HI single head

AF 1 AV sumHH at t

4,260

4,885

5,408

5,664 6,373 7,642

8,037


inventory stock delta_HI single

head

258

176 414 236 257

138

6 21


HI urban single head

AF 1 AV sumHH at t

1,411

1,734

2,065

2,349 2,867 3,709

4,015



single head

84

96 132 149 177

125



WHt single head aw HI

24,424

27,042

28,319 31,864 38,211

40,184


WHt urban single head aw HI

8,672

10,326

11,747 14,334 18,545

20,075

housing start HS single head

WHt delta_HI

283

203 386 268 295

178


single head WHt delta_HI

92

106 120 164 196

145

4 other household 1,000 units

others household urban

8 64

7 81

8 02 8 40 8 40 8 50

8 54

AF doubling rate

1 00

1 00

1 00 1 00 1 00 1 00

1 00

AV vacancy rate

0 01

0 01

0 01 0 01 0 01 0 01

0 01


HI urban others

AF 1 AV sumHH at t

8 59

7 77

7 98 8 36 8 36 8 46

8 50



others 162 95 79 0 20 19



WHt urban others aw HI

0 16

0 16 0 17 0 17 0 17

0 17


others WHt delta_HI

0 32

0 25

0 25 0 17 0 19

0 19

Housing start HS Total

1 2 3 4 1,000 units

1,094 73

463 94 253 79 1,149 23 569 51

323 91

Housing start HS urban

1 2 3 4 urban 1,000 units

501 28

415 53

264 20 503 57 547 64

426 13

6 22

Note indicates negative numbers Housing inventory is stock adjustment annually, while housing start is regarded as the

changing of inventory each period after taking into account the housing withdrawal owing to dismantle or causing fire etc , and

has to be cleared from the inventory

We have developed low income housing need and affordability model, on a spreadsheet

to project the housing inventory and housing starts for all households and urban households The

housing start is a change in housing inventory which is an adjustment between demand and

supply of housing It can be regarded as the excess demand at equilibrium which can exhibit

market signal either positive or negative value given that the house price is always positive This

is because even though the change in inventory is negative the price can never be zero since

there is still stock of house in the market to be cleared by demand side

The foregoing analysis has shown that urbanization that would take place in Thailand in

the coming decades has expressed housing demand in urban area of 415 53 thousand units in

2020 The housing demand would be 503 57 thousand units in 2030 and 426 13 thousand units in

2037 respectively As we have shown in Table 6 1 that low income household deciles 1 4 could

not afford to buy house from the private housing market It is therefore a government role to

provide housing for low income in urban area We will see the affordability of low income in the

next analysis concerning the house price and inverse housing demand

4 Estimation of Inverse demand for house and Affordable House Price

In this section, we are going to estimate the Inverse demand for house applying a logistic

estimation method The demand represents the affordable power of existing house ownership by

households, ex post The analysis covers three areas entire country, municipal area, and Bangkok

metropolitan and vicinities respectively

This analysis is to find the factor which affects the house price It is, in fact, an inverse

housing demand relationship applying data from SES 2007 Our hypothesis is whether the

income of household affects the imputed value of house or price of a house inverse demand for a

house Total income in this study consists of an average wage per month, overtime pay, bonus,

an average money receipt from goods and products per month from all businesses, and an

average operational expenditure per month from all businesses The regression model is

𝐼𝑚𝑝𝑢𝑡𝑒𝑑 𝐻𝑜𝑢𝑠𝑒 𝑃𝑟𝑖𝑐𝑒 =∝𝑖+ 𝐿𝑜𝑔(𝑇𝑜𝑡𝑎𝑙 𝐼𝑛𝑐𝑜𝑚𝑒) 𝑖 + 𝐴𝑔𝑒𝑖 + 𝑀𝑒𝑚𝑏𝑒𝑟𝑠𝑖 + 𝑢𝑖

Data Data Sources

Sales of Real Estate, and National

Housing Authority's Housing Project

Real Estate Information Center REIC

Total income, age, members of a

family, homeownership, housing

expenditure, and house price of survey

respondents

Socio Economic Survey SES in 2007

6 23

Table 6 6 Determination of House Price and Housing Expenditure for Municipal Area

Municipal Area BMR and Vacilities

Variable housepricemuni housepricebkk

l_ttlinc 197953 47 235392 91

members 3442 1383 17488 975

age 11751 579 16658 14

_cons 2037330 2 2511575 8

N 2538 1635

r2 0 11458855 0 13149135

legend p< 1; p< 05; p< 01

It is found that higher percentage change in income level has a positive relationship with

house price for both municipal area and BMR and vicinities area Higher income growth may

result in higher purchasing power to acquire for luxury or a bigger house It seems that growing

age of head will also accumulate more assets and wealth The household can afford to buy a

house with the higher price range

This also means that low income household may find difficulty in acquiring a house with

a higher price and or a large number of mortgage services a month The poor households who

belong to deciles 1 5 cannot access to the housing market even Without proper housing policy

for the poor, they will not be able to find a proper resident In an urban area, the government may

consider launching subsidies such as reducing interest rates for a home loan and assisting

construction costs

Firstly, we experiment with the hypothetical assumption of reducing the interest rates

Average of 6 major banks floating mortgage rate in 2016 is in the range of 6 69 to 6 85 per

year according to information from the Real Estate Information Center REIC The mortgage

interest rate of 7 is set with monthly payment such that the net income ratio does not exceed

33 2 for major banks The affordable monthly payment in the next 30 years, given the monthly

income of ฿ 15,000 and annual interest rate of 7 can be estimated As a result, the affordable

price of a house by the assumed mortgage condition amounts to ฿760,456 a unit If government

2 http www reic or th RealEstateForPeople Topic AdviceHomeLoan02 asp

http://www.reic.or.th/RealEstateForPeople/Topic-AdviceHomeLoan02.asp

6 24

subsidies are assumed, it would help lower the interest rates from 7 to be 6 and 5 , the

affordable price of houses would increase to ฿839,879 ฿933,865 0054 respectively

Table 6 7 The projection of affordable price of houses given an income of ฿15,000

By varying income level of household and interest rates, the affordable price of houses is

higher as the interest rates are lower and individual monthly income increases as shown in Table

below

Table 6 8 The hypothetical affordable price of houses given income

of ฿15,000 20,000 baht per month

Our analysis has found that government may need to assist the low income

household with a monthly income of 15,000 baht to access to government low income housing

provision at 650,000 baht a unit of 24 square meters Flat type by NHA A mortgage service

amount is approximately 4,500 baht a month for 30 years of housing loan with 7 percent

interest rate The low income household is facing hard burden to make a mortgage service if

without government assistance

Scenario No government subsidy Subsidy with rate 1 Subsidy with rate 2

Monthly income 15,000

Monthly payment to net income ratio 0 33

Monthly payment 4,950

Down payment 0

Loan term months 360

Annual rate 0 07 0 06 0 05

Monthly rate 0 005654145 0 004867551 0 004074124

Mortgage amount 760,456 7049 839,879 2775 933,865 0054

Monthly income

10,000 15,000 20,000

Interest rates 7 00 506,971 14 760,456 70 1,013,942 27

6 00 559,919 52 839,879 28 1,119,839 04

5 00 622,576 67 933,865 01 1,245,153 34

6 25

Figure 6 14 Sale, Newly Opened Sale and Accumulated Unsold Houses from 2009 to 2015

While low income urbanites could not access the government housing market, the private

provision of a house in the BMR has shown a slacked demand The unsold units have

accumulated during 2009 to 2015 According to the Real Estate Information Center Thailand ,

the private supply in the housing market has started to show excess supply as result of economic

slowdown The newly opened sale has reduced sharply from 2013 to 2015 by 9 and 11 ,

respectively

As a matter of fact, the house types that poor to lower middle class can access to the

mortgage market are such as low price condo size 28 square meter and medium price condo

size 43 square meter respectively The Condo of size 28 square meters was unrealistically low as

it was an average of both government and private house price They may be located in the remote

area of the province where the cost of land was still cheaper than the urban area The size of 28

and 43 square meters Condo have shown policy intervened trend as compared with low medium

price townhouse The former was a supply provisioned by a public organization like NHA while

the latter's from the private market provision They have a normal trend of rising cost of

construction

We have shown the price per square meter which determined the cost of construction,

management, and sale, interest cost as well profit making etc The housing policy in Thailand has

put balance in the housing market The low income housing policy in Thailand may balance the

rinsing sale price of houses in Thailand to some extent

While luxury condo and luxury detached house are beyond the reach of middle income

class, the medium price townhouse and condo are still good alternatives For low income

household, the choice is still open for low price townhouse of 68 square meters and perhaps

medium price condo 43 square meters as well Our model projection for housing needs and

0

50000

100000

150000

2009 2010 2011 2012 2013 2014 2015

Bangkok and Vicinities

Sale per year (unit) Newly opened sale Accumulated unsolde house

6 26

affordability in the whole country and in particular the urban area has the following policy

implications

1) Most of households belong to medium high income class deciles 5 10th can enter the

private housing market in various types from detached house, Condo, and Townhouse

with larger size and pricing On the contrary, low income households deciles 1 4th

cannot access to housing market by themselves and need assistance from government

2) The role of government is therefore scoped down to concentrate in social investment

role i e the provision of house for low income households

3) The type of housing can be ranged from low price Condo of 28 square meters,

Townhouse for those who can afford to service the mortgage deciles 4th to rental

house for low income in various forms deciles 2 3rd

4) The lowest income deciles households may need special treatment by the government

Figure 6 15 House price by type and size

0

200,000

400,000

600,000

800,000

1,000,000

1,200,000

1,400,000

1,600,000

1,800,000

2,002 2,004 2,006 2,008 2,010 2,012 2,014

Year

Medium Price Townhouse 100 sqM

Medium Price Condo 43 sqM

low price condo 28 sqM

Low price townhouse 68 sqM

Ba

ht

per

Un

it

House price by Type and Size (For Low to Middle Income Class)

Source Agency for Real Estate Affairs 2013 www area co th

http://www.area.co.th/

6 27

Figure 6 15 continued

0

10,000

20,000

30,000

40,000

50,000

2,002 2,004 2,006 2,008 2,010 2,012 2,014

Years

Price per sqM of low price condo size 28 sqm

Price per sqM low price town house size 68 sqm

Price per sqM of luxery detached house size 193 sqM

Price per sq meter of luxury condo size 120 sqM

Price per sqM of medium Price Condo size 43 sqM

Price per SqM Medium Price Townhouse size100 sqM

Price per sqM Medium Price Detached house size 147 sqM

Price per Square Meter of House Types and Sizes

Baht

per

Squ

are

Mete

r

The study here would like to propose how the government can perform social investment

in dwelling for low income households as our main purpose We have therefore to investigate the

government plans on this social investment in the following section

5 Government Effort in Residential development

The government project to assist the low income households has been established by The

National Housing Authority NHA , Ministry of Social Development and Human Security under

several governments Currently, a project named Baan 3 Pracha Rath and Baan Thanarak

Pracharat housing projects' provides loan for low income people to own house with a price

which does not exceed 1 5 million baht The loan is provided by Government Housing Bank

GHB and Government Savings Bank for buying, constructing, or fixing with a specified

amount of money respectively

Recently, the Cabinet has also announced an adjustment in criteria for low income people

without property in their possession It is granted for housing loan that does not exceed 1 5

million baht The project is eligible for employees who earn 20,000 baht a month or less

3 Baan literally means house

6 28

In fact, every government has initiated a similar project for low income housing For

example, a program which has launched in 2003 aimed to solve housing problem of poorest

urban citizens named as the Baan Mankong 4 Collective Housing Program' It has provided

subsidies and soft loan for housing and land So far, a total number of 858 projects has been

approved for 90,813 families

We would like to note the following Stylized Facts

(1) Ownership of House

In 2013, among the total of 20 17 million households reported by the socio economic

survey NSO only 15 01 million households 75 19 percent has owned house and land The rest

5 01 million households have no ownership in one way or another Some households own house

but not land, other build house in public land, rent, hire purchase, reside with others free

conditions etc

(2) Income Distribution and Affordability

The NESDB has reported that only 1 931 million households 41 with average monthly

income of 20,700 baht per month or percentile 60 and can afford to buy a cheap house The rest

of households 2 751 million households 59 with income less than 20,700 baht a month or

lower than percentile 60 desperately need government support This amounts to 2 726 million

households

The NSO reports further that household with monthly income 13,701 20,700 baht

cannot afford a house in the housing market They have to rely on a rental house from the market

or public provision Here, 1 579 million households with monthly income lower than 13,700 baht

are facing the difficulty of settlement The Community Organization Development Institute

CODI has reported further 47 of the low incomes or 791,647 households residing in the slum

area

In fact, the accessibility to a standard qualified house for a low income household is the

most concern of any government Based on a report by the NESDB, 80 percent of land

ownership belongs to highest income group The lowest income group of 20 percent owns only

0 3 percent of the land asset This implies that low income housing is constrained by land price

as well

(3) Government Policy

The current government by the Ministry Human Security and Social Welfare has put

effort to mobilize a 10 year strategic housing development plan 2016 2025 They have tried to

execute a 3 year low income housing development plan 2016 2018 and achieved an immediate

plan in 2016 The cabinet has decided to allow public private investment for low income

4 Literally housing security

6 29

households including low salary government officials as well The plan would also help finance

squatter community along a canal, raising the quality of life of homeless by the non

governmental organization

Following the guideline of SDG 2016 2030 , the government has written up the The 10 Years

Strategic Housing Development Plan 2016 2025 ' This plan aims to promote housing security

that can raise a quality of life of low income households The plan has aimed to provide standard

dwelling unit with proper environment for the community, equipped with basic infrastructure for

2 72 million low income households

The provision of 567,691 units on 27,241 rai of land with a planned budget of

569,524 70 million baht has the following features

1) The housing provision for low income 1 707 million units by NHA and in cooperation

with related agencies They comprise rent and hire purchase sub groups 1 rental house

of 91,657 units for low income, with planned budget of 102,662 21 million baht 2 hire

purchase for low income 1 615 million units, consist of 2 1 421,034 units for low

income, with planned budget of 422,465 49 million baht 2 2 civil servant house 55,000

units, with planned budget of 44,497 million baht 2 3 the public private cooperation

rent and hire purchase or Ban Pracharat 1,139,746 units respectively

2) The housing provision for 1 044 million low income households in both urban and rural

area by CODI 1 044 million units for rent These comprise 1 urban squatter

community and low income earners 692,510 households, with a planned budget of

126,725 84 million baht 2 Rental house for rural low income household 352,000

households, with a budget of 20,349 million baht

3) Target Group by income area types of needs The low income without the property right which can be divided by area and

income level such as

A The household in Bangkok area and perimeter

1 Rental household with 15,301 22,900 baht per month


B Household in provincials area



6 30

C the low income in slum, trespassing community and homeless; the rural low income with a residential problem; and the low rank government officers who need a house

4 Implementation Target by Agencies

Dwelling security for 2,725,924 households comprises

4 1 NHA 1,707,437 housing for low incomes

1 Rental group 91,657 households

2 Hire purchase 1,615,780 households

4 2 the low income in slum trespassing community and homeless implement by CODI 692,510 household

4 3 the rural low income with residential problem Implement by CODI 352,000 household

5 Project Format

The 10 years Housing Development Plan 2016 2025 has set project format in response to target groups need for affordable ability

5 1 The residential development plan for the common low income implement by NHA by cooperation with a related organization in private sector and government sector in an amount of 1,707,437 household consist of 4 categories

1 Quality of life improvement plan rental is developing a rental unit in an amount of 91,657 units in Bangkok and perimeter area 45,359 units and in a rural area 46,298 units

This project format is a rental apartment for the low income with the 3 5 floors residential area has one bedroom with 28 32 square meters the ground floor of the building is Universal' design for the elderly and handicapped

2 Strengthening the housing security plan hire purchase in an amount of 421,034 units in the Bangkok and perimeter 161,248 units and rural area 259,786 units This project format in the Bangkok area is condominium with 4 35 floors In the rural area is a single house double house townhouse and condominium depend on the suitable of the local area the design is using a universal design with the infrastructure

3 Government officer housing project in an amount of 55,000 units in a format of a house for government officials in an amount of 30,000 units and official residence in an amount of 25,000 units

5 2 low income housing urban and rural implement by CODI in cooperation

with the local government for the low income in the amount of 1,044,510 units consisting of

5 2 1 Housing for the slum dwellers and urban low income in the amount of

692,725 84 household consist of 3 projects

6 31

1 Baan Man Khong project in the amount of 680,808 households,

managed in form of cooperative by community

2 Canalside housing project in an amount of 11,004 units, for solve

the trespassing of the canal side communities in Bangkok

3 Homeless quality of life improving project, 698 households 1,395

people to support the homeless center which managed by a

homeless network, to promote their quality of life

5 2 2 Rural low income housing implement by CODI in co operating with the

local government in the amount of 352,000 households which support the renovate the old

house in a rural area or rebuild the old and damage house

5 3 land donated by Department of Social Development and Welfare for the

aforementioned projects in the amount of 960 rai5

6 Investment Budget

The Ministry of Finance will seek fund for the 10 years Residential development plan

2016 2025 See detail below in Table 6 9 which is planned figures

5 1 rai 1,600 square meters or 0 16 hectare

6 32

Table 6 9 10 Years Housing Development Strategic Plan of National Housing Authority, Thailand

2016 2025

A Immediate Plan 2016 2018

Plan Project Units Immediate Plan 3 Years

2016 2017 2018 2016 2018

1 1 Low Income Quality of Life by Rent

1 Rental housing for low income 10,107 26,000

5,261

2,490 40

3,240

1,617 15

1,606

787 85

10,107

4,895 40

2 Rental Housing in Economic Zone

24,000 4,000

2,392 00

4,000

2,512 00

8,000

4,904 00

3 Housing Improvement 1 20,292 334 00

613 76 1,247

1,849 01

1,581

2,462 77

4 Housing Improvement 2 8,255

5 Housing Improvement 3 3,003

Sub Total 91,657 5,595 7,240 6,853 19,688

Investment Source of Fund 3,104 16 4,009 15 5,148 86 12,262 17

●Subsidy from Government 2,103 90 3,299 58 2,742 07 8,145 55

● Loan 386 5 709 57 557 78 1,653 85

● Borrow from Government 613 76 0 1,809 83 2,423 59

●Own Revenue 0 0 39 18 39 18

1 2 Low Income Housing by Hire Purchase

1 Housing Development 1 24,901 13,314

8,975 27

11,587

8,936 80 24,901

17,912 07

Housing Development 9,133 9,133

5,989 42 9,133

5,989 42

2 Housing Development 2 35,000 35,000

29,960 00

35,000

29,960 00

3 Housing Development 3 30,000

4 Housing Development 4 period 1 6

170,000

5 New town 5 48,000

6 33

6 Housing Development along the

Train Route BMR First period

12,000 12,000

4,000

3,260 00

4,000

3,424 00

8,000

6,684 00

7 New Town along the Speed Train

Route Economic Corridor Economic Corridor

80,000

Sub Units 421,034 22,447 15,587 39,000 77,034



● Loan 10,981 65 8,767 82 21,174 60 40,924 07

● Borrow from Government 0 0 0 0

●Own Revenue 1,808 53 1,374 49 3,338 40 6,521 42

1 3 Housing for civil servants

1 Hire purchase

unit cost baht

30,000 3,000

2,328 00

776,000

3,000

2,445 00

815,000

3,000

2,568 00

856,000

9,000

7,341 00

2 Government house for civil

servant

unit cost baht

25,000 5,000

2,845 00

569,000

10,000

5,980 00

598,000

10,000

6,280 00

628,000

25,000

15,105 00

Units 55,000 8,000 13,000 13,000 34,000



● Loan 1,490 40 1,552 50 1,617 00 4,659 90

● Borrow from Government 0

●Own Revenue 232 8 244 5 256 8 734 1

1 4 Public Private Partnership Housing Development

1 Government Private Housing 1,139,746

Grand Total Units 1,707,437 36,042 35,827 58,853 130,722



● Loan 12,858 55 11,029 89 23,349 38 47,237 82

● Borrow from Government 613 76 0 1,809 83 2,423 59

●Own Revenue 2,041 33 1,618 99 3,634 38 7,294 70

6 34

Table 6 10 10 Years Housing Development Strategic Plan of National Housing Authority, Thailand 2016 2025

B Medium Long term Plan 2016 2025

Plan Project Units 3 Years Medium Plan 5 years

2016 20

Long Term Plan 10 years 2016 2025 Total Investment

(million

baht)

2016 2018 2019 2020 2021 2022 2023 2024 2025

1 1 Low Income Quality of Life by Rent

1 Rental housing for low income

10,107 26,000

10,107

4,895 40 4,000

2,768 00

4,000

2,908 00

4,000

3,052 00

4,000

3,204 00

5,000

4,205 00

5,000

4,415 00 4,895 40

20,552 00

2 Rental Housing in Economic Zone

24,000 8,000

4,904 00

7,000

4,925 00

3,000

2,403 00

3,000

2,523 00

3,000

2,649 00 17,404 00

3 Housing Improvement

1

20,292 1,581

2,462 77 5,943

9,754 57 12,768

25,243 37 37,460 71

4 Housing Improvement 2

8,255 4,445

9,186 74 3,810

8,563 36 17,750 10

5 Housing Improvement 3

3,003 490

626 30 2,513

3,973 70 4,600 00

Sub Total 91,657 19,688 7,000 12,943 7,490 19,768 10,958 5,000 8,810 Investment Source of Fund

12,262 17 4,925 00 14,925 57 6,057 30 30,944 37 16,364 44 4,205 00 12,978 36 102,662 21

●Subsidy from Government

8,145 55 3,696 00 3,694 00 3,880 40 4,073 80 2,353 60 3,088 00 3,242 00 32,173 35

● Loan 1,653 85 1,229 00 4,897 53 1,550 60 23,109 12 4,824 10 1,117 00 9,736 36 48,117 56

● Borrow from Government

2,423 59 0 5,116 95 626 3 2,339 69 9,186 74 0 0 19,693 27

●Own Revenue 39 18 0 1,217 09 0 1,421 76 0 0 0 2,678 03

1 2 Low Income Housing by Hire Purchase

1 Housing Development 1

24,901 24,901

17,912 07 17,912 07

Housing Development 9,133 9,133

5,989 42 5,989 42

2 Housing

Development 2

35,000 35,000

29,960 00 29,960 00

3 Housing Development 3

30,000 30,000

26,970 00 26,970 00

4 Housing

Development 4 period 1 6

170,000 25,000

23,600 00

25,000

24,775 00

30,000

31,200 00

30,000

32,760 00

30,000

34,410 00

30,000

36,120 00 182,865 00

5 New town 5 48,000 20,000

18,880 00

28,000

27,748 00 46,628 00

6 35


2016 20


(million

baht)

2016 2018 2019 2020 2021 2022 2023 2024 2025

6 Housing Development along the Train Route BMR First period

12,000 12,000

8,000

6,684 00

4,000

3,596 00

3,000

2,832 00

3,000

2,973 00

3,000

3,120 00

3,000

3,276 00 10,280 00

12,201 00

7 New Town along the

Speed Train Route Economic Corridor Economic Corridor

80,000 20,000

20,800 00

20,000

21,840 00

20,000

22,940 00

20,000

24,080 00 89,660 00

Sub Units 421,034 77,034 34,000 48,000 56,000 53,000 53,000 50,000 50,000 Investment Source of Fund

60,545 49 30,566 00 45,312 00 55,496 00 55,120 00 57,876 00 57,350 00 60,200 00 422,465 49


13,100 00 8,262 00 12,287 00 15,285 80 16,601 00 17,783 00 18,148 00 19,414 00 120,880 80

● Loan 40,924 07 19,247 40 28,493 80 34,660 60 33,007 00 34,305 40 33,467 00 34,766 00 258,871 27


0 0 0 0 0 0 0 0 0

●Own Revenue 6,521 42 3,056 60 4,531 20 5,549 60 5,512 00 5,787 60 5,735 00 6,020 00 42,713 42

1 3 Housing for civil servants

1 Hire purchase

unit cost baht

30,000 9,000

7,341 00

3,000

2,697 00

899,000

3,000

2,832 00

944,000

3,000

2,973 00

991,000

3,000

3,120 00

1,040,00

0

3,000

3,276 00

1,092,00

0

3,000

3,441 00

1,147,00

0

3,000

3,612 00

1,204,00

0

29,292 00

2 Government house for the civil servant

unit cost baht

25,000 25,000

15,105 00 15,105 00

Units 55,000 34,000 3,000 3,000 3,000 3,000 3,000 3,000 3,000 Investment Source

of Fund 22,446 00 2,697 00 2,832 00 2,973 00 3,120 00 3,276 00 3,441 00 3,612 00 44,397 00


17,052 00 743 4 794 4 850 2 910 2 975 1,042 80 1,115 40 23,483 40

6 36


2016 20


(million

baht)

2016 2018 2019 2020 2021 2022 2023 2024 2025

● Loan 4,659 90 1,683 90 1,754 40 1,825 50 1,897 80 1,973 40 2,054 10 2,135 40 17,984 40


0 0

●Own Revenue 734 1 269 7 283 2 297 3 312 327 6 344 1 361 2 2,929 20

1 4 Public Private Partnership Housing Development

1 Government Private Housing

1,139,746 Operated by Ministry of Finance with Cooperation by NHA

Grand Total Units 1,707,437 130,722 44,000 63,943 66,490 75,768 66,958 58,000 61,810 Investment Source

of Fund 95,253 66 38,188 00 63,069 57 64,526 30 89,184 37 77,516 44 64,996 00 76,790 36 569,524 70


38,297 55 12,701 40 16,775 40 20,016 40 21,585 00 21,111 60 22,278 80 23,771 40 176,537 55

● Loan 47,237 82 22,160 30 35,145 73 38,036 70 58,013 92 41,102 90 36,638 10 46,637 76 324,973 23


2,423 59 0 5,116 95 626 3 2,339 69 9,186 74 0 0 19,693 27

●Own Revenue 7,294 70 3,326 30 6,031 49 5,846 90 7,245 76 6,115 20 6,079 10 6,381 20 48,320 65

Source Ministry of Social Development and Human Security 2016 , National Housing 10 Years Strategic Plan 2016 2025

6 37

6 Synthesis and Implications on Social Investment Needs

The foregoing section is a planned supply provision by the National Housing Authority Our

housing needs model is a micro based projection from population and households survey SES as

sources of parameterization It has applied a base line forecast of population as referenced path for

housing needs and affordability of the low income household deciles

The value of social investment needs for low cost housing in Thai in urban area can be

estimated by synthesizing with average unit cost of public housing provision government plan as

follows

In order to estimate the cost of social investment from our micro analysis low income housing

needs in urban area during 2020 2037, we estimate the unit value of house price by extrapolating

from Table 7 8, it assumes government s unit cost of house on average is 0 99 million baht in 2020

It increases to 1 86 and 2 23 million baht per unit in 2035 and 2037 respectively Total cost of

investment during 2020 2037 is in sum 3 487 trillion baht for all urban households

Now, if we assume proportion of poor urban households to be 30 percent, we arrive at the cost

of investment for low income housing in urban area of 1 046 trillion baht If the proportion of low

income households is 16 percent, the social cost of investment is 558 04 billion baht respectively

The methodology can be repeated with the foregoing example for other ASEAN countries

Table 6 11 Estimated Cost of Social Investment on Urban Low Cost Housing

Cost of Social Investment 1,000 Million Baht

Year

Urban

Housing

Start

Units

Unit

Cost

Million

Baht

Value of

Urban

House

thousand

Million

Baht

Assumptions on Poor Household Proportion

poor

40

poor

30

poor

25

poor

20

poor

15 poor 16

Value of

Urban House thousand Million Baht

2020

415 53 0 99 409 85 163 94 122 96 102 46 81 97 61 48 65 58

2025

264 2 1 24 327 61 131 04 98 28 81 90 65 52 49 14 52 42

2030

503 57 1 55 780 53 312 21 234 16 195 13 156 11 117 08 124 89

2035

547 64 1 86 1,018 61 407 44 305 58 254 65 203 72 152 79 162 98

2037

426 13 2 23 951 12 380 45 285 34 237 78 190 22 142 67 152 18

All

2,157 07 1 57 3,487 73 1,395 09 1,046 32 871 93 697 55 523 16 558 04

Note 1 unit cost is extrapolated from Table 6 9; 2 Urban housing start is from our model; 3 value of urban house is 3 1 x 2 ;

4 value of urban house by proportion of poor 4 proportion x 3 respectively

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